[1]} found that linguistic features such as tweet length, average word length, number of punctuation, number of discourse connectives can be useful for detecting abusive language. In this study, we adopt 9 features from their work. Besides the \(n\) -gram features, named entity, and linguistic features, we also adopted emoji and emoticons as additional features, which have been shown to be useful in sentiment analysis tasks [2]}, [3]}. Emoticons are extracted using the s regular expressions by C. Pottshttp://sentiment.christopherpotts.net/tokenizing.html #emoticons.
We also added three emoji sentiment features, which consist of the positive, negative, and overall sentiment scores based on the Emoji Sentiment Ranking [4]}.
